Advancements in wireless communication technology have greatly increased the versatility of today's wireless communication devices. These advancements have enabled wireless communication devices to evolve from simple mobile telephones and pagers into sophisticated computing devices capable of performing a wide variety of functionality such as multimedia recording and playback, event scheduling, word processing, e-commerce, etc. As a result, users of today's wireless communication devices are able to perform a wide range of tasks from a single, portable device.
As the number of wireless communication devices has increased, so has the demand for more robust and intuitive mechanisms for providing input to such devices. While the functionality of wireless communication devices has significantly expanded, the size constrains associated with these devices renders many input devices associated with conventional computing systems, such as keyboards, mouse, etc., to be impractical.
To overcome this limitation, some smart devices use gesture recognition mechanisms to enable a user to provide inputs to the device via motions or gestures. Firstly, there are various parameters based on which a gesture undergoes machine learning techniques for the smart device to recognize the gesture as an input. Secondly, even the user has to learn to perform the gesture accurately for the smart device to recognize it as an input. Hence, there are two stages of learning i.e machine learning to recognize the gesture and user learning to perform the gesture accurately. The common factor between the two learning is the motor activity of the user. Consider users hand with reduced motor activity and the user not able to provide the gesture input accurately enough for the machine to recognize the input. Hence, the machine learning has to be increased to adapt to users activity. This would need increased processing power and battery resources of the smart device. Also it would result in delay and poor user experience.
The issues mainly faced in providing gesture recommendation are that the machine may not recognize the user input accurately if there is a reduced motor activity and there is no mechanism to provide suggestions to the user to either change the gesture or improve the gesture when the machine does not detect the gesture.